






'''simple docstring'''





import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
						from test_module.custom_tokenization_fast import CustomTokenizerFast


class  snake_case__      (					unittest.TestCase):




					def 		A      (  self  :		Any      )    ->							List[str]:
								# A mock response for an HTTP head request to emulate server down
								UpperCAmelCase_      :       Optional[int]          =       mock.Mock()
								UpperCAmelCase_      :       int          =       5_00
								UpperCAmelCase_      :       Tuple          =       {}
								UpperCAmelCase_      :       Optional[int]          =       HTTPError
								UpperCAmelCase_      :       str          =       {}

								# Download this model to make sure it's in the cache.
								UpperCAmelCase_      :       Any          =       BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert'''      )

								# Under the mock environment we get a 500 error when trying to reach the tokenizer.
								with mock.patch('''requests.Session.request'''				, return_value=_A      ) as mock_head:
											UpperCAmelCase_      :       Tuple          =       BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert'''      )
											# This check we did call the fake head request
											mock_head.assert_called()




					@require_tokenizers
					def 		A      (  self  :		Any      )    ->							Union[str, Any]:
								# A mock response for an HTTP head request to emulate server down
								UpperCAmelCase_      :       Union[str, Any]          =       mock.Mock()
								UpperCAmelCase_      :       List[Any]          =       5_00
								UpperCAmelCase_      :       Tuple          =       {}
								UpperCAmelCase_      :       List[Any]          =       HTTPError
								UpperCAmelCase_      :       Optional[int]          =       {}

								# Download this model to make sure it's in the cache.
								UpperCAmelCase_      :       Any          =       GPTaTokenizerFast.from_pretrained('''gpt2'''      )

								# Under the mock environment we get a 500 error when trying to reach the tokenizer.
								with mock.patch('''requests.Session.request'''				, return_value=_A      ) as mock_head:
											UpperCAmelCase_      :       Tuple          =       GPTaTokenizerFast.from_pretrained('''gpt2'''      )
											# This check we did call the fake head request
											mock_head.assert_called()




					def 		A      (  self  :		Tuple      )    ->							Union[str, Any]:
								# This test is for deprecated behavior and can be removed in v5
								try:
											UpperCAmelCase_      :       int          =       tempfile.mktemp()
											with open(_A				, '''wb'''      ) as f:
														http_get('''https://huggingface.co/albert-base-v1/resolve/main/spiece.model'''				, _A      )

											UpperCAmelCase_      :       Union[str, Any]          =       AlbertTokenizer.from_pretrained(_A      )
								finally:
											os.remove(_A      )

								# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
								# the current folder and have the right name.
								if os.path.isfile('''tokenizer.json'''      ):
											# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
											return
								try:
											with open('''tokenizer.json'''				, '''wb'''      ) as f:
														http_get('''https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json'''				, _A      )
											UpperCAmelCase_      :       List[Any]          =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''      )
											# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
											self.assertEqual(tokenizer.vocab_size				, 10_00      )
											# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

								finally:
											os.remove('''tokenizer.json'''      )





					def 		A      (  self  :		str      )    ->							Dict:
								# This test is for deprecated behavior and can be removed in v5
								UpperCAmelCase_      :       List[Any]          =       AlbertTokenizer.from_pretrained('''https://huggingface.co/albert-base-v1/resolve/main/spiece.model'''      )






@is_staging_test
class  snake_case__      (					unittest.TestCase):
					a_        =					["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]




					@classmethod
					def 		A      (  cls  :		Optional[int]      )    ->							Optional[int]:
								UpperCAmelCase_      :       List[str]          =       TOKEN
								HfFolder.save_token(_A      )




					@classmethod
					def 		A      (  cls  :		Optional[Any]      )    ->							Union[str, Any]:
								try:
											delete_repo(token=cls._token				, repo_id='''test-tokenizer'''      )
								except HTTPError:
											pass

								try:
											delete_repo(token=cls._token				, repo_id='''valid_org/test-tokenizer-org'''      )
								except HTTPError:
											pass

								try:
											delete_repo(token=cls._token				, repo_id='''test-dynamic-tokenizer'''      )
								except HTTPError:
											pass




					def 		A      (  self  :		Tuple      )    ->							str:
								with tempfile.TemporaryDirectory() as tmp_dir:
											UpperCAmelCase_      :       List[str]          =       os.path.join(_A				, '''vocab.txt'''      )
											with open(_A				, '''w'''				, encoding='''utf-8'''      ) as vocab_writer:
														vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]      )      )
											UpperCAmelCase_      :       Any          =       BertTokenizer(_A      )

								tokenizer.push_to_hub('''test-tokenizer'''				, use_auth_token=self._token      )
								UpperCAmelCase_      :       Union[str, Any]          =       BertTokenizer.from_pretrained(F"{USER}/test-tokenizer"      )
								self.assertDictEqual(new_tokenizer.vocab				, tokenizer.vocab      )

								# Reset repo
								delete_repo(token=self._token				, repo_id='''test-tokenizer'''      )

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
											tokenizer.save_pretrained(_A				, repo_id='''test-tokenizer'''				, push_to_hub=_A				, use_auth_token=self._token      )

								UpperCAmelCase_      :       Optional[Any]          =       BertTokenizer.from_pretrained(F"{USER}/test-tokenizer"      )
								self.assertDictEqual(new_tokenizer.vocab				, tokenizer.vocab      )




					def 		A      (  self  :		List[str]      )    ->							Dict:
								with tempfile.TemporaryDirectory() as tmp_dir:
											UpperCAmelCase_      :       Dict          =       os.path.join(_A				, '''vocab.txt'''      )
											with open(_A				, '''w'''				, encoding='''utf-8'''      ) as vocab_writer:
														vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]      )      )
											UpperCAmelCase_      :       Dict          =       BertTokenizer(_A      )

								tokenizer.push_to_hub('''valid_org/test-tokenizer-org'''				, use_auth_token=self._token      )
								UpperCAmelCase_      :       str          =       BertTokenizer.from_pretrained('''valid_org/test-tokenizer-org'''      )
								self.assertDictEqual(new_tokenizer.vocab				, tokenizer.vocab      )

								# Reset repo
								delete_repo(token=self._token				, repo_id='''valid_org/test-tokenizer-org'''      )

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
											tokenizer.save_pretrained(
											    _A				, repo_id='''valid_org/test-tokenizer-org'''				, push_to_hub=_A				, use_auth_token=self._token      )

								UpperCAmelCase_      :       str          =       BertTokenizer.from_pretrained('''valid_org/test-tokenizer-org'''      )
								self.assertDictEqual(new_tokenizer.vocab				, tokenizer.vocab      )





					@require_tokenizers
					def 		A      (  self  :		int      )    ->							List[Any]:
								CustomTokenizer.register_for_auto_class()
								with tempfile.TemporaryDirectory() as tmp_dir:
											UpperCAmelCase_      :       Optional[int]          =       os.path.join(_A				, '''vocab.txt'''      )
											with open(_A				, '''w'''				, encoding='''utf-8'''      ) as vocab_writer:
														vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]      )      )
											UpperCAmelCase_      :       Union[str, Any]          =       CustomTokenizer(_A      )

								# No fast custom tokenizer
								tokenizer.push_to_hub('''test-dynamic-tokenizer'''				, use_auth_token=self._token      )

								UpperCAmelCase_      :       Dict          =       AutoTokenizer.from_pretrained(F"{USER}/test-dynamic-tokenizer"				, trust_remote_code=_A      )
								# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
								self.assertEqual(tokenizer.__class__.__name__				, '''CustomTokenizer'''      )

								# Fast and slow custom tokenizer
								CustomTokenizerFast.register_for_auto_class()
								with tempfile.TemporaryDirectory() as tmp_dir:
											UpperCAmelCase_      :       int          =       os.path.join(_A				, '''vocab.txt'''      )
											with open(_A				, '''w'''				, encoding='''utf-8'''      ) as vocab_writer:
														vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]      )      )

											UpperCAmelCase_      :       Optional[Any]          =       BertTokenizerFast.from_pretrained(_A      )
											bert_tokenizer.save_pretrained(_A      )
											UpperCAmelCase_      :       Dict          =       CustomTokenizerFast.from_pretrained(_A      )

								tokenizer.push_to_hub('''test-dynamic-tokenizer'''				, use_auth_token=self._token      )

								UpperCAmelCase_      :       List[Any]          =       AutoTokenizer.from_pretrained(F"{USER}/test-dynamic-tokenizer"				, trust_remote_code=_A      )
								# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
								self.assertEqual(tokenizer.__class__.__name__				, '''CustomTokenizerFast'''      )
								UpperCAmelCase_      :       List[str]          =       AutoTokenizer.from_pretrained(
								    F"{USER}/test-dynamic-tokenizer"				, use_fast=_A				, trust_remote_code=_A      )
								# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
								self.assertEqual(tokenizer.__class__.__name__				, '''CustomTokenizer'''      )






class  snake_case__      (					unittest.TestCase):




					def 		A      (  self  :		str      )    ->							List[str]:
								UpperCAmelCase_      :       Tuple          =       Trie()
								trie.add('''Hello 友達'''      )
								self.assertEqual(trie.data				, {'''H''': {'''e''': {'''l''': {'''l''': {'''o''': {''' ''': {'''友''': {'''達''': {'''''': 1}}}}}}}}}      )
								trie.add('''Hello'''      )
								trie.data
								self.assertEqual(trie.data				, {'''H''': {'''e''': {'''l''': {'''l''': {'''o''': {'''''': 1, ''' ''': {'''友''': {'''達''': {'''''': 1}}}}}}}}}      )




					def 		A      (  self  :		List[Any]      )    ->							str:
								UpperCAmelCase_      :       Optional[int]          =       Trie()
								self.assertEqual(trie.split('''[CLS] This is a extra_id_100'''      )				, ['''[CLS] This is a extra_id_100''']      )
								trie.add('''[CLS]'''      )
								trie.add('''extra_id_1'''      )
								trie.add('''extra_id_100'''      )
								self.assertEqual(trie.split('''[CLS] This is a extra_id_100'''      )				, ['''[CLS]''', ''' This is a ''', '''extra_id_100''']      )




					def 		A      (  self  :		List[str]      )    ->							Optional[Any]:
								UpperCAmelCase_      :       Optional[int]          =       Trie()
								trie.add('''A'''      )
								self.assertEqual(trie.split('''ABC'''      )				, ['''A''', '''BC''']      )
								self.assertEqual(trie.split('''BCA'''      )				, ['''BC''', '''A''']      )




					def 		A      (  self  :		int      )    ->							Dict:
								UpperCAmelCase_      :       List[Any]          =       Trie()
								trie.add('''TOKEN]'''      )
								trie.add('''[SPECIAL_TOKEN]'''      )
								self.assertEqual(trie.split('''This is something [SPECIAL_TOKEN]'''      )				, ['''This is something ''', '''[SPECIAL_TOKEN]''']      )




					def 		A      (  self  :		List[str]      )    ->							str:
								UpperCAmelCase_      :       Dict          =       Trie()
								trie.add('''A'''      )
								trie.add('''P'''      )
								trie.add('''[SPECIAL_TOKEN]'''      )
								self.assertEqual(trie.split('''This is something [SPECIAL_TOKEN]'''      )				, ['''This is something ''', '''[SPECIAL_TOKEN]''']      )




					def 		A      (  self  :		Optional[Any]      )    ->							int:
								UpperCAmelCase_      :       Tuple          =       Trie()
								trie.add('''AB'''      )
								trie.add('''B'''      )
								trie.add('''C'''      )
								self.assertEqual(trie.split('''ABC'''      )				, ['''AB''', '''C''']      )




					def 		A      (  self  :		Union[str, Any]      )    ->							Dict:
								UpperCAmelCase_      :       Dict          =       Trie()
								trie.add('''ABC'''      )
								trie.add('''B'''      )
								trie.add('''CD'''      )
								self.assertEqual(trie.split('''ABCD'''      )				, ['''ABC''', '''D''']      )





					def 		A      (  self  :		Optional[int]      )    ->							List[str]:
								# Even if the offsets are wrong, we necessarily output correct string
								# parts.
								UpperCAmelCase_      :       int          =       Trie()
								UpperCAmelCase_      :       int          =       trie.cut_text('''ABC'''				, [0, 0, 2, 1, 2, 3]      )
								self.assertEqual(_A				, ['''AB''', '''C''']      )










'''simple docstring'''





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def __UpperCAmelCase   (					A					:			Optional[int]		)    ->   List[Any]:
			# This defines a "chinese character" as anything in the CJK Unicode block:
			#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
			#
			# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
			# despite its name. The modern Korean Hangul alphabet is a different block,
			# as is Japanese Hiragana and Katakana. Those alphabets are used to write
			# space-separated words, so they are not treated specially and handled
			# like the all of the other languages.
			if (
			    (cp >= 0X4E_00 and cp <= 0X9F_FF)
			    or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
			    or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
			    or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
			    or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
			    or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
			    or (cp >= 0XF9_00 and cp <= 0XFA_FF)
			    or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
			):  #
						return True

			return False
def __UpperCAmelCase   (					A					:			str		)    ->   Optional[Any]:
			# word like '180' or '身高' or '神'
			for char in word:
						UpperCAmelCase_      :       str          =       ord(A		)
						if not _is_chinese_char(A		):
									return 0
			return 1
def __UpperCAmelCase   (					A					:			List[str]		)    ->   Dict:
			UpperCAmelCase_      :       Optional[Any]          =       set()

			for token in tokens:
						UpperCAmelCase_      :       str          =       len(A		) > 1 and is_chinese(A		)
						if chinese_word:
									word_set.add(A		)
			UpperCAmelCase_      :       Optional[int]          =       list(A		)
			return word_list
def __UpperCAmelCase   (					A					:			List[str]			,       A					:			set()		)    ->   Optional[Any]:
			if not chinese_word_set:
						return bert_tokens
			UpperCAmelCase_      :       Dict          =       max([len(A		) for w in chinese_word_set]		)

			UpperCAmelCase_      :       List[str]          =       bert_tokens
			UpperCAmelCase_    ,      UpperCAmelCase_      :       List[Any]          =       0, len(A		)
			while start < end:
						UpperCAmelCase_      :       str          =       True
						if is_chinese(bert_word[start]		):
									UpperCAmelCase_      :       str          =       min(end - start			,       A		)
									for i in range(A			,       1			,       -1		):
												UpperCAmelCase_      :       Tuple          =       ''''''.join(bert_word[start : start + i]		)
												if whole_word in chinese_word_set:
															for j in range(start + 1			,       start + i		):
																		UpperCAmelCase_      :       Union[str, Any]          =       '''##''' + bert_word[j]
															UpperCAmelCase_      :       Any          =       start + i
															UpperCAmelCase_      :       Optional[int]          =       False
															break
						if single_word:
									start += 1
			return bert_word
def __UpperCAmelCase   (					A					:			List[str]			,       A					:			LTP			,       A					:			BertTokenizer		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       Optional[int]          =       []

			for i in range(0			,       len(A		)			,       1_0_0		):
						UpperCAmelCase_      :       int          =       ltp_tokenizer.pipeline(lines[i : i + 1_0_0]			,       tasks=['''cws''']		).cws
						UpperCAmelCase_      :       Any          =       [get_chinese_word(A		) for r in res]
						ltp_res.extend(A		)
			assert len(A		) == len(A		)

			UpperCAmelCase_      :       Tuple          =       []
			for i in range(0			,       len(A		)			,       1_0_0		):
						UpperCAmelCase_      :       Optional[int]          =       bert_tokenizer(lines[i : i + 1_0_0]			,       add_special_tokens=A			,       truncation=A			,       max_length=5_1_2		)
						bert_res.extend(res['''input_ids''']		)
			assert len(A		) == len(A		)

			UpperCAmelCase_      :       Any          =       []
			for input_ids, chinese_word in zip(A			,       A		):
						UpperCAmelCase_      :       Union[str, Any]          =       []
						for id in input_ids:
									UpperCAmelCase_      :       Union[str, Any]          =       bert_tokenizer._convert_id_to_token(A		)
									input_tokens.append(A		)
						UpperCAmelCase_      :       List[str]          =       add_sub_symbol(A			,       A		)
						UpperCAmelCase_      :       Any          =       []
						# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
						for i, token in enumerate(A		):
									if token[:2] == "##":
												UpperCAmelCase_      :       int          =       token[2:]
												# save chinese tokens' pos
												if len(A		) == 1 and _is_chinese_char(ord(A		)		):
															ref_id.append(A		)
						ref_ids.append(A		)

			assert len(A		) == len(A		)

			return ref_ids
def __UpperCAmelCase   (					A					:			List[Any]		)    ->   Tuple:
			# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
			# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
			with open(args.file_name			,       '''r'''			,       encoding='''utf-8'''		) as f:
						UpperCAmelCase_      :       Optional[Any]          =       f.readlines()
			UpperCAmelCase_      :       List[str]          =       [line.strip() for line in data if len(A		) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
			UpperCAmelCase_      :       Tuple          =       LTP(args.ltp		)  # faster in GPU device
			UpperCAmelCase_      :       Union[str, Any]          =       BertTokenizer.from_pretrained(args.bert		)

			UpperCAmelCase_      :       Optional[int]          =       prepare_ref(A			,       A			,       A		)

			with open(args.save_path			,       '''w'''			,       encoding='''utf-8'''		) as f:
						UpperCAmelCase_      :       str          =       [json.dumps(A		) + '''\n''' for ref in ref_ids]
						f.writelines(A		)


if __name__ == "__main__":
						_UpperCamelCase						:						Optional[Any]  =					argparse.ArgumentParser(description='prepare_chinese_ref')
						parser.add_argument(
						    '--file_name',
						    required=False,
						    type=str,
						    default='./resources/chinese-demo.txt',
						    help='file need process, same as training data in lm',
						)
						parser.add_argument(
						    '--ltp',
						    required=False,
						    type=str,
						    default='./resources/ltp',
						    help='resources for LTP tokenizer, usually a path',
						)
						parser.add_argument(
						    '--bert',
						    required=False,
						    type=str,
						    default='./resources/robert',
						    help='resources for Bert tokenizer',
						)
						parser.add_argument(
						    '--save_path',
						    required=False,
						    type=str,
						    default='./resources/ref.txt',
						    help='path to save res',
						)

						_UpperCamelCase						:						Any  =					parser.parse_args()
						main(args)


